In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of...In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.展开更多
Policy training against diverse opponents remains a challenge when using Multi-Agent Reinforcement Learning(MARL)in multiple Unmanned Combat Aerial Vehicle(UCAV)air combat scenarios.In view of this,this paper proposes...Policy training against diverse opponents remains a challenge when using Multi-Agent Reinforcement Learning(MARL)in multiple Unmanned Combat Aerial Vehicle(UCAV)air combat scenarios.In view of this,this paper proposes a novel Dominant and Non-dominant strategy sample selection(DoNot)mechanism and a Local Observation Enhanced Multi-Agent Proximal Policy Optimization(LOE-MAPPO)algorithm to train the multi-UCAV air combat policy and improve its generalization.Specifically,the LOE-MAPPO algorithm adopts a mixed state that concatenates the global state and individual agent's local observation to enable efficient value function learning in multi-UCAV air combat.The DoNot mechanism classifies opponents into dominant or non-dominant strategy opponents,and samples from easier to more challenging opponents to form an adaptive training curriculum.Empirical results demonstrate that the proposed LOE-MAPPO algorithm outperforms baseline MARL algorithms in multi-UCAV air combat scenarios,and the DoNot mechanism leads to stronger policy generalization when facing diverse opponents.The results pave the way for the fast generation of cooperative strategies for air combat agents with MARLalgorithms.展开更多
In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training s...In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.展开更多
At present, most of high-quality rice varieties are susceptible to blast diseases. In this study, Gumei 2, a rice variety carrying broad-spectrum resistant Pi25 gene, was used as the donor, with Xiangwanxian 13 taken ...At present, most of high-quality rice varieties are susceptible to blast diseases. In this study, Gumei 2, a rice variety carrying broad-spectrum resistant Pi25 gene, was used as the donor, with Xiangwanxian 13 taken as the receptor and recurrent parent which is also of good quality but highly susceptible to rice blast, to improve the rice blast resistance of Xiangwanxian 13 by crossing and backcrossing based on molecular marker-assisted selection. The results showed that the resistance of the improved strains （i.e. Xiang C72, Xiang C76 and Xiang C77） to blast diseases had been enhanced signifcantly through feld resistance identifcation, equaling the resistance level of Gumei 2, and the main agronomic and quality-related traits of these improved strains had been restored to the level of Xiangwanxian 13, except the chalkiness and cooking-related traits, which suggested similar genomic loci of Xiang C72, Xiang C76 and Xiang C77 to those of Xiangwanxian 13. These three improved strains （i.e. Xiang C72, Xiang C76 and Xiang C77） can provide good intermediate materials for breeding elite varieties with high grain yields and superior blast resistance.展开更多
Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to cap...Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.展开更多
This paper develops a parameter-expanded Monte Carlo EM (PX-MCEM) algorithm to perform maximum likelihood estimation in a multivariate sample selection model. In contrast to the current methods of estimation, the prop...This paper develops a parameter-expanded Monte Carlo EM (PX-MCEM) algorithm to perform maximum likelihood estimation in a multivariate sample selection model. In contrast to the current methods of estimation, the proposed algorithm does not directly depend on the observed-data likelihood, the evaluation of which requires intractable multivariate integrations over normal densities. Moreover, the algorithm is simple to implement and involves only quantities that are easy to simulate or have closed form expressions.展开更多
For mode selection in a quantum cascade laser(QCL),we demonstrate an anti-symmetric sampled grating(ASG).The wavelength of the-1-th mode of this laser has been blue-shifted more than 75 nm(~10 cm^(-1))compared with th...For mode selection in a quantum cascade laser(QCL),we demonstrate an anti-symmetric sampled grating(ASG).The wavelength of the-1-th mode of this laser has been blue-shifted more than 75 nm(~10 cm^(-1))compared with that of an ordinary sampled grating laser with an emission wavelength of approximately 8.6μm,when the periodicities within both the base grating and the sample grating are kept constant.Under this condition,an improvement in the continuous tuning capability of the QCL array is ensured.The ASG structure is fabricated in holographic exposure and optical photolithography,thereby enhancing its flexibility,repeatability,and cost-effectiveness.The wavelength modulation capability of the two channels of the grating is insensitive to the variations in channel size,assuming that the overall waveguide width remains constant.The output wavelength can be tailored freely within a certain range by adjusting the width of the ridge and the material of the cladding layer.展开更多
Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton plan...Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.展开更多
For a long time,Chinese construction industry has had disadvantages such as waste of resources,environmental pollution,long construction period,great influence by weather,low enterprise efficiency and poor benefit.Whi...For a long time,Chinese construction industry has had disadvantages such as waste of resources,environmental pollution,long construction period,great influence by weather,low enterprise efficiency and poor benefit.While being able to solve the disadvantages of traditional buildings,prefabricated buildings,as an important starting point to achieve the carbon peaking and carbon neutrality goals,are far superior to traditional buildings in green and low-carbon,energy conservation and emission reduction,which can accelerate the transformation and upgrading of the construction industry.In recent years,the government has vigorously promoted prefabricated buildings,but the implementation of prefabricated buildings has been frequently hindered and the development of prefabricated buildings in China has not achieve the expected goal in many aspects.Under the high-quality development strategy proposed by the central government,how to promote the high-quality development of prefabricated buildings in China is one of the key difficulties of Chinese construction industry in the future.Therefore,it is necessary to proceed from the connotation of the high-quality development of prefabricated buildings,establish an evaluation model for the high-quality development level of prefabricated buildings through AHP and comprehensive fuzzy evaluation to master the high-quality development level of prefabricated buildings in China,and explore the reasonable paths selection forthe high-quality development of prefabricated buildings in China.展开更多
The complex and uncertain relationship among failures was always ignored in failure sample selection based on traditional testability demonstration experimental method. A failure pervasion model is founded based on fu...The complex and uncertain relationship among failures was always ignored in failure sample selection based on traditional testability demonstration experimental method. A failure pervasion model is founded based on fuzzy probability Petri net (FPPN) which can depict the propagation and pervasion relation among failures,then failure pervasion intensity is defined,the process of failure pervasion was depicted based on k-step fault pervasion algorithm and the pervasion intensity was expressed by a value. The method of sample selection based on failure pervasion intensity and failure rate is introduced into the process of sample selection. The practical application shows that the sample set selected based on failure pervasion intensity and failure rate can represent the failure set adequately.展开更多
Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resu...Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.展开更多
A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and m...A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and machine learning models,as well as in the extent of LSP performance enhancement after their coupling.To overcome these uncertainties,this study takes Wuning county of China as a case area,collecting 24 conditioning factors and 379 landslides data.Four non-landslide sample selection strategies,namely random selection,low-slope,buffer zone,and semi-supervised strategies,are then combined with landslide samples in a 1:1 ratio to serve as input variables for constructing LSP models using support vector machine(SVM),logistic regression(LR),random forest(RF)and extreme gradient boosting(XGBoost).Finally,the uncertainty of semi-supervised machine learning coupled models with a 1:2 ratio of landslide to non-landslide samples is analyzed and compared.The results show that:(1)The semi-supervised and low-slope strategies demonstrate higher prediction accuracy compared to the buffer zone and random selection strategies.Moreover,the RF coupled models are the most reliable,followed by the XGBoost,SVM,and LR coupled models;(2)Compared to a 1:1 ratio,a 1:2 ratio of landslide to non-landslide samples significantly improves prediction accuracy,suggesting that appropriately increasing the proportion of non-landslide samples helps to mitigate overfitting and enhance the identification of landslide samples;and(3)LSP is more sensitive to non-landslide sample selection strategies than to the choice of machine learning models.In conclusion,prioritizing reliable non-landslide samples is crucial for improving accuracy of LSP.展开更多
Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique f...Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.展开更多
The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimizat...The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.展开更多
This study examines the impact of farmers’cooperatives participation and technology adoption on their economic welfare in China.A double selectivity model(DSM)is applied to correct for sample selection bias stemming ...This study examines the impact of farmers’cooperatives participation and technology adoption on their economic welfare in China.A double selectivity model(DSM)is applied to correct for sample selection bias stemming from both observed and unobserved factors,and a propensity score matching(PSM)method is applied to calculate the agricultural income difference with counter factual analysis using survey data from 396 farmers in 15 provinces in China.The findings indicate that farmers who join farmer cooperatives and adopt agricultural technology can increase agricultural income by 2.77 and 2.35%,respectively,compared with those non-participants and non-adopters.Interestingly,the effect on agricultural income is found to be more significant for the low-income farmers than the high-income ones,with income increasing 5.45 and 4.51%when participating in farmer cooperatives and adopting agricultural technology,respectively.Our findings highlight the positive role of farmer cooperatives and agricultural technology in promoting farmers’economic welfare.Based on the findings,government policy implications are also discussed.展开更多
Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data se...Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.展开更多
A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classif...A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classification boundary; meanwhile, within a batch, examples labeled previously fail to provide instructive information for the selection of the rest. As a result, using the examples selected in batch mode for model refinement will jeopardize the classification performance. Based on the duality between feature space and parameter space under the SVM active learning fi：amework, dynamic batch selective sampling is proposed to address the problem. We select a batch of examples dynamically, using the examples labeled previously as guidance for further selection. In this way, the selection of feedback examples is determined by both the existing classification model and the examples labeled previously. Encouraging experimental results demonstrate the effectiveness of the proposed algorithm.展开更多
As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring sys...As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.展开更多
This paper describes preparation, characterization and electrochemical performance of novel planar miniaturized all-solid-state (ASS) screen-printed potentiometric sensors for the detection of Ca2+ ions in environment...This paper describes preparation, characterization and electrochemical performance of novel planar miniaturized all-solid-state (ASS) screen-printed potentiometric sensors for the detection of Ca2+ ions in environmental samples. Screen-printed graphite-based ion-selective electrodes (ISEs) and screen-printed reference electrodes based on silver-containing pastes have been applied in a space saving manner on common ceramic substrates with small dimensions. Applications to environmental samples are shown by direct potentiometry and potentiometric titrations in real water samples. Conducting polymers (CPs) have been used as solid-contact materials and as intermediate layer between the polyvinyl chloride (PVC)-containing ion-selective membrane and the graphite-containing substrate. Different diamides have been incorporated into the PVC membrane. In the range from 10-4 mol/L to 10-1 mol/L, the ISEs show linear slopes of 27 mV/decade, which is close to the Nernstian response. Moreover, the ISEs have response times of 6 months. The novel potentiometric ASS sensors enable simple and exact Ca2+ determinations in real samples.展开更多
Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological ...Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological conditions.Traditional sampling strategies commonly used in landslide susceptibility models can lead to a misrepresentation of the distribution of negative samples,causing a deviation from actual geological conditions.This,in turn,negatively affects the discriminative ability and generalization performance of the models.To address this issue,we propose a novel approach for selecting negative samples to enhance the quality of machine learning models.We choose the Liangshan Yi Autonomous Prefecture,located in southwestern Sichuan,China,as the case study.This area,characterized by complex terrain,frequent tectonic activities,and steep slope erosion,experiences recurrent landslides,making it an ideal setting for validating our proposed method.We calculate the contribution values of environmental factors using the relief algorithm to construct the feature space,apply the Target Space Exteriorization Sampling(TSES)method to select negative samples,calculate landslide probability values by Random Forest(RF)modeling,and then create regional landslide susceptibility maps.We evaluate the performance of the RF model optimized by the Environmental Factor Selection-based TSES(EFSTSES)method using standard performance metrics.The results indicated that the model achieved an accuracy(ACC)of 0.962,precision(PRE)of 0.961,and an area under the curve(AUC)of 0.962.These findings demonstrate that the EFSTSES-based model effectively mitigates the negative sample imbalance issue,enhances the differentiation between landslide and non-landslide samples,and reduces misclassification,particularly in geologically complex areas.These improvements offer valuable insights for disaster prevention,land use planning,and risk mitigation strategies.展开更多
文摘In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.
文摘Policy training against diverse opponents remains a challenge when using Multi-Agent Reinforcement Learning(MARL)in multiple Unmanned Combat Aerial Vehicle(UCAV)air combat scenarios.In view of this,this paper proposes a novel Dominant and Non-dominant strategy sample selection(DoNot)mechanism and a Local Observation Enhanced Multi-Agent Proximal Policy Optimization(LOE-MAPPO)algorithm to train the multi-UCAV air combat policy and improve its generalization.Specifically,the LOE-MAPPO algorithm adopts a mixed state that concatenates the global state and individual agent's local observation to enable efficient value function learning in multi-UCAV air combat.The DoNot mechanism classifies opponents into dominant or non-dominant strategy opponents,and samples from easier to more challenging opponents to form an adaptive training curriculum.Empirical results demonstrate that the proposed LOE-MAPPO algorithm outperforms baseline MARL algorithms in multi-UCAV air combat scenarios,and the DoNot mechanism leads to stronger policy generalization when facing diverse opponents.The results pave the way for the fast generation of cooperative strategies for air combat agents with MARLalgorithms.
基金supported by the National Natural Science Foundation of China(62371049)。
文摘In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.
基金Supported by Special Fund for National Modern Agro-Industrial Technology System Construction(nycytx-001)National Key Research&Development Project(2016YFD0100101-12)+1 种基金Natural Science Foundation of Hunan Province(2016JJ6061)Natural Science Foundation of China(31600998)~~
文摘At present, most of high-quality rice varieties are susceptible to blast diseases. In this study, Gumei 2, a rice variety carrying broad-spectrum resistant Pi25 gene, was used as the donor, with Xiangwanxian 13 taken as the receptor and recurrent parent which is also of good quality but highly susceptible to rice blast, to improve the rice blast resistance of Xiangwanxian 13 by crossing and backcrossing based on molecular marker-assisted selection. The results showed that the resistance of the improved strains （i.e. Xiang C72, Xiang C76 and Xiang C77） to blast diseases had been enhanced signifcantly through feld resistance identifcation, equaling the resistance level of Gumei 2, and the main agronomic and quality-related traits of these improved strains had been restored to the level of Xiangwanxian 13, except the chalkiness and cooking-related traits, which suggested similar genomic loci of Xiang C72, Xiang C76 and Xiang C77 to those of Xiangwanxian 13. These three improved strains （i.e. Xiang C72, Xiang C76 and Xiang C77） can provide good intermediate materials for breeding elite varieties with high grain yields and superior blast resistance.
基金National Natural Science Foundations of China(Nos.U1162202,61222303)National High-Tech Research and Development Program of China(No.2013AA040701)the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)
文摘Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.
文摘This paper develops a parameter-expanded Monte Carlo EM (PX-MCEM) algorithm to perform maximum likelihood estimation in a multivariate sample selection model. In contrast to the current methods of estimation, the proposed algorithm does not directly depend on the observed-data likelihood, the evaluation of which requires intractable multivariate integrations over normal densities. Moreover, the algorithm is simple to implement and involves only quantities that are easy to simulate or have closed form expressions.
基金Project supported by the National Basic Research Program of China (Grant No. 2021YFB3201900)in part by the National Natural Science Foundation of China (Grant Nos. 61991430, 61774146, 61790583,61627822, and 61774150)in part by the Key Projects of the Chinese Academy of Sciences (Grant Nos. 2018147, YJKYYQ20190002, QYZDJ-SSW-JSC027,XDB43000000)
文摘For mode selection in a quantum cascade laser(QCL),we demonstrate an anti-symmetric sampled grating(ASG).The wavelength of the-1-th mode of this laser has been blue-shifted more than 75 nm(~10 cm^(-1))compared with that of an ordinary sampled grating laser with an emission wavelength of approximately 8.6μm,when the periodicities within both the base grating and the sample grating are kept constant.Under this condition,an improvement in the continuous tuning capability of the QCL array is ensured.The ASG structure is fabricated in holographic exposure and optical photolithography,thereby enhancing its flexibility,repeatability,and cost-effectiveness.The wavelength modulation capability of the two channels of the grating is insensitive to the variations in channel size,assuming that the overall waveguide width remains constant.The output wavelength can be tailored freely within a certain range by adjusting the width of the ridge and the material of the cladding layer.
基金supported by the National Natural Science Foundation of China[grant number 42101342]Third Comprehensive Scientific Expedition to Xinjiang[grant number 2021XJKK1403].
文摘Cotton is one of the most significant cash crops in the world,and it is also the main source of natural fiber for textiles.It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accurately on a fine scale.However,previous research studies have predominantly concentrated on specific years using remote sensing data.Challenges still exist in the extraction of cotton areas for long time series with high accuracy.To address this issue,a novel cotton sample selection method was proposed and the machine learning method is employed to effectively identify the long time series cotton planting areas at a 30-m resolution scale.Bortala and Shuanghe in Xinjiang,China,were selected as the study cases to demonstrate the approach.Specifically,the cropland in this study was extracted by using an object-oriented classification method with Landsat images and the results were optimized as the vectorized boundary of croplands.Then,the cotton samples were selected using the Normalized Difference Vegetation Index(NDVI)series of Moderate Resolution Imaging Spectroradiometer(MODIS)based on its phenological characteristics.Next,cotton was identified based on the croplands from 2000 to 2020 by using the machine learning model.Finally,the performance was evaluated,and the spatiotemporal distribution characteristics of cotton planting areas were analyzed.The results showed that the proposed approach can achieve high accuracy at a fine spatial resolution.The performance evaluation indicated the applicability and suitability of the method,there is a good correlation between the extracted cotton areas and statistical data,and the cotton area of the study area showed an increasing trend.The cotton spatial distribution pattern developed from dispersion to agglomeration.The proposed approach and the derived 30-m cotton maps can provide a scientific reference for the optimization of agricultural management.
文摘For a long time,Chinese construction industry has had disadvantages such as waste of resources,environmental pollution,long construction period,great influence by weather,low enterprise efficiency and poor benefit.While being able to solve the disadvantages of traditional buildings,prefabricated buildings,as an important starting point to achieve the carbon peaking and carbon neutrality goals,are far superior to traditional buildings in green and low-carbon,energy conservation and emission reduction,which can accelerate the transformation and upgrading of the construction industry.In recent years,the government has vigorously promoted prefabricated buildings,but the implementation of prefabricated buildings has been frequently hindered and the development of prefabricated buildings in China has not achieve the expected goal in many aspects.Under the high-quality development strategy proposed by the central government,how to promote the high-quality development of prefabricated buildings in China is one of the key difficulties of Chinese construction industry in the future.Therefore,it is necessary to proceed from the connotation of the high-quality development of prefabricated buildings,establish an evaluation model for the high-quality development level of prefabricated buildings through AHP and comprehensive fuzzy evaluation to master the high-quality development level of prefabricated buildings in China,and explore the reasonable paths selection forthe high-quality development of prefabricated buildings in China.
基金Sponsored by the"11th 5-Year Plan"Advanced Research Fund of a National Ministerial Level Project (51317040102)
文摘The complex and uncertain relationship among failures was always ignored in failure sample selection based on traditional testability demonstration experimental method. A failure pervasion model is founded based on fuzzy probability Petri net (FPPN) which can depict the propagation and pervasion relation among failures,then failure pervasion intensity is defined,the process of failure pervasion was depicted based on k-step fault pervasion algorithm and the pervasion intensity was expressed by a value. The method of sample selection based on failure pervasion intensity and failure rate is introduced into the process of sample selection. The practical application shows that the sample set selected based on failure pervasion intensity and failure rate can represent the failure set adequately.
基金Supported by the Zimin Institute for Engineering Solutions Advancing Better Lives。
文摘Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.42202278,42407241)Natural Science Foundation of Jiangxi Province(Grant No.20242BAB20238).
文摘A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and machine learning models,as well as in the extent of LSP performance enhancement after their coupling.To overcome these uncertainties,this study takes Wuning county of China as a case area,collecting 24 conditioning factors and 379 landslides data.Four non-landslide sample selection strategies,namely random selection,low-slope,buffer zone,and semi-supervised strategies,are then combined with landslide samples in a 1:1 ratio to serve as input variables for constructing LSP models using support vector machine(SVM),logistic regression(LR),random forest(RF)and extreme gradient boosting(XGBoost).Finally,the uncertainty of semi-supervised machine learning coupled models with a 1:2 ratio of landslide to non-landslide samples is analyzed and compared.The results show that:(1)The semi-supervised and low-slope strategies demonstrate higher prediction accuracy compared to the buffer zone and random selection strategies.Moreover,the RF coupled models are the most reliable,followed by the XGBoost,SVM,and LR coupled models;(2)Compared to a 1:1 ratio,a 1:2 ratio of landslide to non-landslide samples significantly improves prediction accuracy,suggesting that appropriately increasing the proportion of non-landslide samples helps to mitigate overfitting and enhance the identification of landslide samples;and(3)LSP is more sensitive to non-landslide sample selection strategies than to the choice of machine learning models.In conclusion,prioritizing reliable non-landslide samples is crucial for improving accuracy of LSP.
基金supported in part by the National Natural Science Foundation of China(61379049,61772120)
文摘Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.
基金Sponsored by the Natural Science Foundation of Liaoning Province in China(Grant No.20022106).
文摘The numerical calculation method is widely used in the evaluation of slope stability,but it cannot take the randomness and fuzziness into account that exist in rock and soil engineering objectively.The fuzzy optimization theory is thus introduced to the evaluation of slope stability by this paper and a method of fuzzy optimal selection of similar slopes is put forward to analyze slope stability.By comparing the relative membership degrees that the evaluated object sample of slope is similar to the source samples of which the stabilities are detected clearly,the source sample with the maximal relative membership degree will be chosen as the best similar one to the object sample,and the stability of the object sample can be evaluated by that of the best similar source sample.In the process many uncertain influential factors are considered and characteristics and knowledge of the source samples are obtained.The practical calculation indicates that it can achieve good results to evaluate slope stability by using this method.
基金the Special Project of Major Theoretical Research and Interpretation of Philosophy and Social Sciences of Chongqing Municipal Education Commission,China(19SKZDZX15)the Key Project of Humanities and Social Sciences Research of Chongqing Education Commission,China(18SKSJ003)the Funding for Cultivating Major Projects in Humanities and Social Sciences of Southwest University,China(SWU1809009)。
文摘This study examines the impact of farmers’cooperatives participation and technology adoption on their economic welfare in China.A double selectivity model(DSM)is applied to correct for sample selection bias stemming from both observed and unobserved factors,and a propensity score matching(PSM)method is applied to calculate the agricultural income difference with counter factual analysis using survey data from 396 farmers in 15 provinces in China.The findings indicate that farmers who join farmer cooperatives and adopt agricultural technology can increase agricultural income by 2.77 and 2.35%,respectively,compared with those non-participants and non-adopters.Interestingly,the effect on agricultural income is found to be more significant for the low-income farmers than the high-income ones,with income increasing 5.45 and 4.51%when participating in farmer cooperatives and adopting agricultural technology,respectively.Our findings highlight the positive role of farmer cooperatives and agricultural technology in promoting farmers’economic welfare.Based on the findings,government policy implications are also discussed.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA040608)National Natural Science Foundation of China(Nos.61473279,61004131)the Development of Scientific Research Equipment Program of Chinese Academy of Sciences(No.YZ201247)
文摘Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.
文摘A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classification boundary; meanwhile, within a batch, examples labeled previously fail to provide instructive information for the selection of the rest. As a result, using the examples selected in batch mode for model refinement will jeopardize the classification performance. Based on the duality between feature space and parameter space under the SVM active learning fi：amework, dynamic batch selective sampling is proposed to address the problem. We select a batch of examples dynamically, using the examples labeled previously as guidance for further selection. In this way, the selection of feedback examples is determined by both the existing classification model and the examples labeled previously. Encouraging experimental results demonstrate the effectiveness of the proposed algorithm.
基金supported by National Key Scientific Instrument and Equipment Development Project of China,Grant Nos.2013YQ220643the National 863 Program of China,Grant Nos.2014AA06A503.
文摘As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.
文摘This paper describes preparation, characterization and electrochemical performance of novel planar miniaturized all-solid-state (ASS) screen-printed potentiometric sensors for the detection of Ca2+ ions in environmental samples. Screen-printed graphite-based ion-selective electrodes (ISEs) and screen-printed reference electrodes based on silver-containing pastes have been applied in a space saving manner on common ceramic substrates with small dimensions. Applications to environmental samples are shown by direct potentiometry and potentiometric titrations in real water samples. Conducting polymers (CPs) have been used as solid-contact materials and as intermediate layer between the polyvinyl chloride (PVC)-containing ion-selective membrane and the graphite-containing substrate. Different diamides have been incorporated into the PVC membrane. In the range from 10-4 mol/L to 10-1 mol/L, the ISEs show linear slopes of 27 mV/decade, which is close to the Nernstian response. Moreover, the ISEs have response times of 6 months. The novel potentiometric ASS sensors enable simple and exact Ca2+ determinations in real samples.
基金supported by Natural Science Research Project of Anhui Educational Committee(2023AH030041)National Natural Science Foundation of China(42277136)Anhui Province Young and Middle-aged Teacher Training Action Project(DTR2023018).
文摘Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological conditions.Traditional sampling strategies commonly used in landslide susceptibility models can lead to a misrepresentation of the distribution of negative samples,causing a deviation from actual geological conditions.This,in turn,negatively affects the discriminative ability and generalization performance of the models.To address this issue,we propose a novel approach for selecting negative samples to enhance the quality of machine learning models.We choose the Liangshan Yi Autonomous Prefecture,located in southwestern Sichuan,China,as the case study.This area,characterized by complex terrain,frequent tectonic activities,and steep slope erosion,experiences recurrent landslides,making it an ideal setting for validating our proposed method.We calculate the contribution values of environmental factors using the relief algorithm to construct the feature space,apply the Target Space Exteriorization Sampling(TSES)method to select negative samples,calculate landslide probability values by Random Forest(RF)modeling,and then create regional landslide susceptibility maps.We evaluate the performance of the RF model optimized by the Environmental Factor Selection-based TSES(EFSTSES)method using standard performance metrics.The results indicated that the model achieved an accuracy(ACC)of 0.962,precision(PRE)of 0.961,and an area under the curve(AUC)of 0.962.These findings demonstrate that the EFSTSES-based model effectively mitigates the negative sample imbalance issue,enhances the differentiation between landslide and non-landslide samples,and reduces misclassification,particularly in geologically complex areas.These improvements offer valuable insights for disaster prevention,land use planning,and risk mitigation strategies.
